Jack Nugent will present his General Exam "Benchmarking the Robustness of Monocular Depth Estimation with Procedural Scene Perturbations" on Friday, May 8, 2026 at 2:00 PM in Friend 202. Committee Members: Jia Deng (advisor), Felix Heide, Szymon Rusinkiewicz Abstract: Monocular depth estimation, predicting depth from a single image, is a fundamental computer vision task that has seen rapid progress in recent years. Though the task is inherently ambiguous, models are increasingly capable of producing accurate metric estimates, sharp predictions, and precise local geometry. As these models are deployed, it becomes increasingly important to rigorously measure their accuracy and their robustness to conditions that may appear in the real world. To systematically analyze model performance, we introduce a new Procedural Depth Evaluation benchmark. We draw on recent advances in photorealistic procedural scene generation to programmatically generate 3D scenes. We then apply controlled perturbations that vary targeted scene factors while preserving the relevant depth-estimation challenge. For example, we alter light intensity, color, and direction while ensuring the entire scene remains visible. By measuring the error on these perturbed scenes, we quantify robustness to a particular perturbation. Our benchmark tests robustness to various object, camera, material, and lighting changes. We find that no model is robust to all these perturbations, and that the most accurate models are not the most robust. This benchmark provides a way to diagnose failure modes in depth estimation models that are not captured by standard evaluations. Reading List: https://docs.google.com/document/d/1LQd3WEYciYDrX_uSbtOBH0IsUuQPGlsOqkPw7Erc... Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.